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🚨🚨🚨 [SuperPoint] Fix keypoint coordinate output and add post processing (#33200)

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* feat: Added int conversion and unwrapping

* test: added tests for post_process_keypoint_detection of SuperPointImageProcessor

* docs: changed docs to include post_process_keypoint_detection method and switched from opencv to matplotlib

* test: changed test to not depend on SuperPointModel forward

* test: added missing require_torch decorator

* docs: changed pyplot parameters for the keypoints to be more visible in the example

* tests: changed import torch location to make test_flax and test_tf

* Revert "tests: changed import torch location to make test_flax and test_tf"

This reverts commit 39b32a2f69500bc7af01715fc7beae2260549afe.

* tests: fixed import

* chore: applied suggestions from code review

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* tests: fixed import

* tests: fixed import (bis)

* tests: fixed import (ter)

* feat: added choice of type for target_size and changed tests accordingly

* docs: updated code snippet to reflect the addition of target size type choice in post process method

* tests: fixed imports (...)

* tests: fixed imports (...)

* style: formatting file

* docs: fixed typo from image[0] to image.size[0]

* docs: added output image and fixed some tests

* Update docs/source/en/model_doc/superpoint.md

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* fix: included SuperPointKeypointDescriptionOutput in TYPE_CHECKING if statement and changed tests results to reflect changes to SuperPoint from absolute keypoints coordinates to relative

* docs: changed SuperPoint's docs to print output instead of just accessing

* style: applied make style

* docs: added missing output type and precision in docstring of post_process_keypoint_detection

* perf: deleted loop to perform keypoint conversion in one statement

* fix: moved keypoint conversion at the end of model forward

* docs: changed SuperPointInterestPointDecoder to SuperPointKeypointDecoder class name and added relative (x, y) coordinates information to its method

* fix: changed type hint

* refactor: removed unnecessary brackets

* revert: SuperPointKeypointDecoder to SuperPointInterestPointDecoder

* Update docs/source/en/model_doc/superpoint.md

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

---------

Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
This commit is contained in:
StevenBucaille
2024-10-29 10:36:03 +01:00
committed by GitHub
parent 655bec2da7
commit a1835195d1
5 changed files with 146 additions and 22 deletions
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54
tests/models/superpoint/test_image_processing_superpoint.py
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@@ -16,7 +16,7 @@ import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import (
ImageProcessingTestMixin,
@@ -24,6 +24,11 @@ from ...test_image_processing_common import (
)
if is_torch_available():
import torch
from transformers.models.superpoint.modeling_superpoint import SuperPointKeypointDescriptionOutput
if is_vision_available():
from transformers import SuperPointImageProcessor
@@ -70,6 +75,23 @@ class SuperPointImageProcessingTester(unittest.TestCase):
torchify=torchify,
)
def prepare_keypoint_detection_output(self, pixel_values):
max_number_keypoints = 50
batch_size = len(pixel_values)
mask = torch.zeros((batch_size, max_number_keypoints))
keypoints = torch.zeros((batch_size, max_number_keypoints, 2))
scores = torch.zeros((batch_size, max_number_keypoints))
descriptors = torch.zeros((batch_size, max_number_keypoints, 16))
for i in range(batch_size):
random_number_keypoints = np.random.randint(0, max_number_keypoints)
mask[i, :random_number_keypoints] = 1
keypoints[i, :random_number_keypoints] = torch.rand((random_number_keypoints, 2))
scores[i, :random_number_keypoints] = torch.rand((random_number_keypoints,))
descriptors[i, :random_number_keypoints] = torch.rand((random_number_keypoints, 16))
return SuperPointKeypointDescriptionOutput(
loss=None, keypoints=keypoints, scores=scores, descriptors=descriptors, mask=mask, hidden_states=None
)
@require_torch
@require_vision
@@ -110,3 +132,33 @@ class SuperPointImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
pre_processed_images = image_processor.preprocess(image_inputs)
for image in pre_processed_images["pixel_values"]:
self.assertTrue(np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...]))
@require_torch
def test_post_processing_keypoint_detection(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
image_inputs = self.image_processor_tester.prepare_image_inputs()
pre_processed_images = image_processor.preprocess(image_inputs, return_tensors="pt")
outputs = self.image_processor_tester.prepare_keypoint_detection_output(**pre_processed_images)
def check_post_processed_output(post_processed_output, image_size):
for post_processed_output, image_size in zip(post_processed_output, image_size):
self.assertTrue("keypoints" in post_processed_output)
self.assertTrue("descriptors" in post_processed_output)
self.assertTrue("scores" in post_processed_output)
keypoints = post_processed_output["keypoints"]
all_below_image_size = torch.all(keypoints[:, 0] <= image_size[1]) and torch.all(
keypoints[:, 1] <= image_size[0]
)
all_above_zero = torch.all(keypoints[:, 0] >= 0) and torch.all(keypoints[:, 1] >= 0)
self.assertTrue(all_below_image_size)
self.assertTrue(all_above_zero)
tuple_image_sizes = [(image.size[0], image.size[1]) for image in image_inputs]
tuple_post_processed_outputs = image_processor.post_process_keypoint_detection(outputs, tuple_image_sizes)
check_post_processed_output(tuple_post_processed_outputs, tuple_image_sizes)
tensor_image_sizes = torch.tensor([image.size for image in image_inputs]).flip(1)
tensor_post_processed_outputs = image_processor.post_process_keypoint_detection(outputs, tensor_image_sizes)
check_post_processed_output(tensor_post_processed_outputs, tensor_image_sizes)